This paper explores the design method of integrating Wireless Rechargeable Sensor Network (WRSN) technology into the comprehensive experiment of the Signals and Systems course. By analyzing current of the Signals and Systems course and its experimental teaching, and combining the characteristics of WRSN technology, we design an innovative experimental design of bridge safety monitoring system based on WRSN. This experimental design aims to help students grasp core concepts of Signals and Systems through practical application scenarios, while fostering problem-solving skills. Results demonstrate that the comprehensive experiment based on WRSN can effectively improve students' practical ability, innovative thinking, and teamwork spirit, supporting the cultivation of future-oriented engineers.

Due to the advantages of device ubiquity, natural interaction and privacy preservation, acoustic-based gesture input has received widespread attention. Researchers have proposed various techniques for different applications. However, the existing work has shortcomings of heavy data-collection overhead, non-continuous input, and performance degradation in crossuser scenarios. To overcome these shortcomings, we propose UltraWrite, an acoustic-based gesture input system that only needs extremely low data-collection overhead, supports continuous input, and achieves high cross-user recognition accuracy. The key idea of our solution is to synthesize training data of continuous gestures from isolated ones, build a lightweight continuous gesture recognition model based on connectionist temporal classification (CTC) mechanism, and design a novel decoupled model training strategy to improve its cross-user recognition capability. We have implemented prototype systems on commercial devices and conducted comprehensive experiments to evaluate their performance. The results show that UltraWrite achieves an average top-1 word accuracy of 99.3% and top-1 word error rate of 0.34%. In addition, we have also evaluated UltraWrite's robustness to the sensing distance, angle, background noise, and device. The results reveal that UltraWrite possesses strong robustness to these factors.

Due to the advantages of acoustic sensing such as device ubiquity, hands-free interaction and privacy security, acoustic-based gesture input techniques have gained extensive at-tention and many excellent works have been proposed. However, these works have the following shortcomings: high cost of system construction, non-continuous input, and degraded performance in cross-user scenarios. To overcome the above shortcomings, we propose UltraWrite, a continuous gesture input system that needs rather low system construction cost, supports continuous input, and achieves high cross-user recognition performance. The key idea of our solution is to synthesize the data of continuous gestures from isolated ones, and build an end-to-end continuous gesture recognition model by introducing the connectionist temporal classification (CTC) mechanism widely used in natural language processing. We have implemented the system on a commercial tablet and conducted comprehensive experiments to evaluate its performance. The results demonstrate that UltraWrite achieves an average word accuracy of 99.3% and word error rate of 0.34% when considering only the first output candidate word. In addition, we have also evaluated the system's robustness to background noise, sensing distance and angle. The results reveal that UltraWrite displays strong robustness to these factors.

Most recently, gesture recognition has increasingly attracted intense academic and industrial interest due to its various applications in daily life, such as home automation, mobile games. Present approaches for gesture recognition, mainly including vision-based, sensor-based and RF-based, all have certain limitations which hinder their practical use in some scenarios. For example, the vision-based approaches fail to work well in poor light conditions and the sensor-based ones require users to wear devices. To address these, we propose WiG in this paper, a device-free gesture recognition system based solely on Commercial Off-The-Shelf (COTS) WiFi infrastructures and devices. Compared with existing Radio Frequency (RF)-based systems, WiG stands out for its systematic simplicity, extremely low cost and high practicability. We implemented WiG in indoor environment and conducted experiments to evaluate its performance in two typical scenarios. The results demonstrate that WiG can achieve an average recognition accuracy of 92% in line-of-sight scenario and average accuracy of 88% in the none-line-of sight scenario.

Cayley data centers are well known patterns of completely wireless data centers (WDCs). However, low link reliability and link interference will affect the construction of virtual networks. This paper proposes a virtual resource mapping algorithm on the basis of Cayley structures. First, we analyze the characteristics of Cayley WDCs and model networks in WDCs, where a virtual network is modeled as a traditional undirected graph, while a physical topology is modeled as a directed graph. Second, we propose a virtual resource mapping and coloring algorithm based on link interference called VRMCA-LI. We build a connection interference matrix for each node and use a coloring method to avoid interference. VRMCA-LI uses the same color for the nodes that are within the transmitting angle of a sending node and whose signal-to-noise ratio is less than a threshold. These nodes with the same color cannot be allocated to virtual nodes at the same time. The allocation of nodes and links are concurrent, which performs dynamic adjustment to save mapping time. Third, our experimental results show that VRMCA-LI outperforms WVNEA-LR and PG-VNE in terms of mapping time of virtual nodes, acceptance rate of virtual networks, and average node utilization rate.